<p>
	Before the BMV paper was published in 2009, a number of theories on the pricing premium for stocks with idiosyncratic skewness existed, but lacked supporting empirical evidence of the relationship between idiosyncratic skewness and returns. BMV fills this void by estimating a model of predicted skewness and using predicted skewness to explain the cross-section of returns. The paper finds that lagged idiosyncratic volatility is a stronger predictor of skewness than lagged idiosyncratic skewness.
</p>

<p>
	In this implementation, we rely on idiosyncratic volatility and skewness to predict idiosyncratic skewness. Interested users can build from this implementation by trying the following extensions:
</p>

<ol>
	<li> Including a number of firm-specific variables to improve predictive power for expected idiosyncratic skewness;
	<li> Using different investment horizons such as 3 months, 6 months, 1 year;
	<li> Adding more lags in the time-series regression for both expected and historical idiosyncratic skewness.
</ol>